*Suggestion: Do author order by alphabetical surname.

Title: This Template is further to the briefing Please do alphabetical order by surname.

Mission: Team’s Mission

Choose from: Ageing Society / Clean Growth / AI and Data Economy / Future of Mobility

Stakeholder: Whose call is addressing this issue? If you say government, be specific about which department. If you say industry, say specifically which sector. Ideally also state in one sentence why.

How is your analysis related to your theme? What is your research question? If you need to cite for context, don’t waste time on bibliography, add an html link.

Context

#USe tools as you need to. 
#Some in the teams has experience running pythong with R if you are more comfy with that.

#Ploty is nice to use, but screenshot from excell with do too. 
library(plotly)
#library(circlize)
library(knitr)
library(geojsonio)
library(sp)
library(tmap)

Datasets

Datasets you used to approach this problem.

What data is available to measure progress on your problem? See Zara’s notebook from the first day. Provide tables or figures from these datasets that tell us something about your challenge.

Analysis

Suggestion: Do a broadsweep of the situation first.

x = c("Stage 1","Stage 2","Stage 3","Stage 4", "Stage 5")
y = c("15","10","3","17","5")

p <- plot_ly(y=y, x=x, histfunc='sum', type = "histogram") %>%
  layout(
    title = "Bogus Buiding Cycle Descriptive Stat",
    yaxis=list(type='linear'))

p

Source: Caveat to keep in mind: Reported by Bonnie once, August 2019.

Suggestion: Then get specific with analysis and modelling

#Feel free to use downloaded data. 
#Opening csvs: http://rprogramming.net/read-csv-in-r/
#Loading excels: https://www.rdocumentation.org/packages/openxlsx/versions/4.1.0.1/topics/read.xlsx

regions_json <- geojson_read("https://opendata.arcgis.com/datasets/8d3a9e6e7bd445e2bdcc26cdf007eac7_1.geojson", what = "sp")

t_wu02Ew  <- read.csv(file = "https://www.nomisweb.co.uk/api/v01/dataset/NM_1206_1.data.csv?date=latest&usual_residence=2013265921...2013265930&place_of_work=2013265921...2013265930&age=0...6&measures=20100", header=TRUE)

reg_var <- xtabs(OBS_VALUE ~ USUAL_RESIDENCE_CODE + AGE_NAME, data=t_wu02Ew)
reg_var <- as.data.frame.matrix(reg_var) 
reg_var <- reg_var[1]
names(reg_var) <- "Relevant Stuff"

#This from Bonnie's Tutorial
#More tmap power here: https://cran.r-project.org/web/packages/tmap/vignettes/tmap-getstarted.html

regions_json@data <- merge(regions_json@data, reg_var, by.x= "rgn15cd", by.y=0 )
tmap_mode("view")
## tmap mode set to interactive viewing
tm_shape(regions_json) + tm_polygons(col="Relevant Stuff")
## Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3

Your recommendation

This will be very group dependent. Use what you need to get your point across. But whatever you do, summarise your recommendation in 1 sentence.

General Recommendation: E.g Reduce gray energry in new builds by using prefabricated elements created in designated areas based on expected building need.

Data Recommendation: Collect machine readable specifications of production and origin of shipping for all prefabricated elements. Specification should include total mass of coal use.

Talk about why you have chosen to throw money at this in particular. Is it particularly easy to do?

Ethics : Although buildings use more enegery when in use, the choice of this recommendation was made because it would also lower energy costs, whilst avoiding the collection of sensititve building level data.

Further Notes for Discussion

Include any cool stuff you found revelant.